DrEricKerrigan

Contact

Assistant

Miss Michelle Hammond+44 (0)20 7594 6281

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Location

1108cElectrical EngineeringSouth Kensington Campus

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Summary

Summary

My main area of research is the development of theory and methods for model predictive control (MPC) to handle nonlinearities and uncertainties in a systematic fashion. MPC is the most widely-implemented advanced control technique in industry. In MPC, a sequence of optimal control and estimation problems need to be solved in real-time - this requires orders of magnitude more computational resources than classical control methods. My particular area of expertise is in the design of efficient numerical methods and computer architectures for solving these optimal control and estimation problems in real-time.

I am also interested in developing new multi-objective optimization methods for the co-design of the overall closed-loop system. These methods can be used to explore how the complexities of the algorithms, computer architecture and physical realization need to be traded off to satisfy given system-wide performance specifications.

I have a joint appointment in the Department of Electrical & Electronic Engineering and the Department of Aeronautics. My theoretical research is therefore motivated by a wide variety of problems in the design of aerospace, renewable energy and information systems. Applications include scheduling of computation and communication in aerial and mobile robotic networks, aerodynamic drag reduction over aerofoils, gust and load alleviation in wind turbine blades and control of small satellites.

See my Google Scholar page for my most recent publications and preprints.

PHD STUDENTSHIPS AVAILABLE

If you are interested in doing a PhD under my supervision in the development of novel numerical methods and architectures for model predictive control and dynamic optimization, please contact me with your CV, transcript of your academic record and a personal statement. We will have a number of open studentships, which can be tailored to areas of mutual interest, available for applications submitted after October 2019 for start dates in 2020.

Software

SPLIT: C code generation for Model Predictive Control based on operator splitting methods. SPLIT is capable of generating both software and hardware-oriented C code to allow quick prototyping of optimization algorithms on conventional CPUs and field-programmable gate arrays (FPGAs). See our paper for more details.